5 research outputs found
K-nearest Neighbor Rule: A Replica Selection Approach in Grid Environment
No full textGrid technology is developed to share data across many organizations
in different geographical locations. Data replication is a good technique
that helps to move data because it caches data closer to users. The idea of
replication is to store copies in different locations so it can be easily
recovered if one copy at one location is lost. Moreover, if data can be kept
closer to user via replication, data access performance can be improved
dramatically. When different sites hold replicas, there are significant
benefits realized when selecting the best replica. Network performance plays a
major role in selecting a replica. However, current research shows that other
factors such as disk I/O also plays an important role in file transfer. In
this paper, we describe a new optimization technique that considers both disk
throughput and network latencies when selecting the best replica. Previous
history of data transfer can help in predicting the best site that can hold
replica. The k-nearest neighbor rule is one such predictive technique. In this
technique, when a new request arrives for best replica, it looks at all
previous data to find a subset of previous file requests that are similar to
it and uses them to predict the best site that can hold replica. In this
work, we implement and test the k-nearest algorithm for various file access
patterns and compare results with the traditional replica catalog based
model. The results demonstrate that our model outperforms the traditional
model for sequential and unitary random file access requests.We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at [email protected]
Dynamics of Nitrogen Mineralization by Organic and Inorganic Amendments Through Enzyme Activity of Microbial Community in Laboratory Incubation
No full textChemical fertilizers provide an immediate nitrogen supply but require repeated application at critical growth stages; however, excessive chemical fertilizer application harms the environment. In contrast, organic fertilizers release nitrogen gradually for a long time, and microbial fertilizers enhance nutrient availability. This study investigated the effects of integrating chemical nitrogen (CN), poultry manure (PM), and microbial fertilizer (MBF) on soil nitrogen availability and microbial activity. Eight treatments were applied: T0 (control), T1 (100% CN), T2 (100% CN + MBF), T3 (75% CN + 25% PM + MBF), T4 (50% CN + 50% PM + MBF), T5 (25% CN + 75% PM + MBF), T6 (100% PM + MBF), and T7 (100% PM). Soil nitrogen fractions, microbial biomass, enzyme activities, and phospholipid fatty acid (PLFA) composition were analyzed. Integrated treatments improved nitrogen availability compared to sole CN application, with T4 showing the highest NO₃--N accumulation. Additionally, T4 increased total nitrogen, organic carbon, and microbial biomass, enhancing soil fertility. Enzymatic activities, including urease, catalase, invertase, and cellulase, responded positively to the integrated treatments, reflecting improved soil health. PLFA analysis revealed shifts in microbial community composition, highlighting the role of PM in promoting microbial diversity and biomass. These findings highlight that blending 50% CN and 50% PM with MBF balances immediate and sustained nitrogen release while stimulating microbial diversity and soil enzyme functions and improves overall soil health, making it a promising strategy for sustainable soil fertility management and reducing chemical fertilizer dependency
Morphological and physiological responses of maize to varying nitrogen sources and stress levels in hydroponic systems: a comparative study
No full textA hydroponic experiment was conducted at the Precision and Automated Agriculture Laboratory, Department of Agronomy and Agricultural Extension, University of Rajshahi, from April to August 2023. The objective was to investigate the morphological and physiological responses of maize to varying nitrogen sources and stress levels in hydroponic systems. The experiment comprised three nitrogen treatments: CN (100% chemical nitrogen as 2 mM NH4NO3), ON (100% organic nitrogen as 4 mM glycine), and LN (low nitrogen as 10% of 2 mM NH4NO3 chemical nitrogen solution). The popular maize variety NH7720 (marketed by Syngenta Bangladesh Limited) was used. The experiments followed a completely randomised design with three replications. The CN treatment consistently outperformed the ON and LN treatments in various growth-related parameters, including plant height (72.73 cm), leaf area (295.54 cm²), shoot dry weight (0.65 g/plant), total chlorophyll content (3.11 mg/g), and shoot (11.06%) and root (10.82%) protein content, indicating that adequate nitrogen treatment stimulated strong growth and development in maize plants. Conversely, the LN treatment exhibited a superior shoot-to-root ratio (85.43%), proline accumulation (188.01 µg/g), number of root tips (21.25), root length (31.65 cm), root network area (619.10 cm²), root diameter (5.63 mm), root volume (13944.71 mm³), and root surface area (3705.51 mm²). These results suggest that under nitrogen-deficient conditions, maize plants allocate resources to root development and stress tolerance mechanisms. The organic nitrogen (ON) treatment showed intermediate results, being statistically similar to both the CN and LN treatments across a range of characteristics, suggesting that organic nitrogen or glycine might be less effective than chemical nitrogen or ammonium nitrate in promoting optimal maize growth
INVESTIGATING SOYBEAN GROWTH, YIELD AND OIL TRAITS PERFORMANCE WITH POTASSIUM AND SULPHUR AMENDMENTS
No full text<p><span>The study was conducted at the Agronomy Field Laboratory of the Department of Agronomy and Agricultural Extension at the University of Rajshahi, from December 2019 to March 2020. The primary objective was to evaluate the effects of potassium and sulphur on the growth, yield, and oil content of the BINA Soybean-1 variety. The experimental design used was a Randomized Complete Block Design (RCBD) with three replications to ensure the reliability and accuracy of the results. Various rates of potassium (K) and sulphur (S) were tested: K<sub>1</sub> (20 kg ha<sup>-1</sup>), K<sub>2</sub> (40 kg ha<sup>-1</sup>), K<sub>3</sub> (60 kg ha<sup>-1</sup>), S<sub>1</sub> (20 kg ha<sup>-1</sup>), S<sub>2</sub> (30 kg ha<sup>-1</sup>), and S<sub>3</sub> (40 kg ha<sup>-1</sup>). Throughout the study period, all necessary intercultural operations were performed to maintain optimal growth conditions for the soybean plants. Upon reaching full maturity, the soybean crops were harvested, and the results indicated that the highest rate of potassium application (60 kg ha<sup>-1</sup>) significantly improved several growth parameters. These included plant height, number of branches, leaf area, pod formation, seed yield, and oil content. Similarly, the application of sulphur at the highest rate (40 kg ha<sup>-1</sup>) also showed substantial improvements across these parameters. The most notable finding was the synergistic effect observed with the combined application of potassium at 60 kg ha<sup>-1</sup> and sulphur at 40 kg ha<sup>-1</sup>. This combination produced the most favorable outcomes in terms of growth and yield. Specifically, the maximum oil content recorded was 26.72%, and the protein content of 49.49% was highest with the application of these higher nutrient levels.</span></p>
